Overview

Dataset statistics

Number of variables18
Number of observations1000000
Missing cells419628
Missing cells (%)2.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory137.3 MiB
Average record size in memory144.0 B

Variable types

Numeric6
DateTime2
Categorical6
Text4

Alerts

Status is highly imbalanced (53.2%)Imbalance
Title has 139876 (14.0%) missing valuesMissing
Artist has 139876 (14.0%) missing valuesMissing
Length has 139876 (14.0%) missing valuesMissing
EventID is uniformly distributedUniform
EventID has unique valuesUnique
TrackId has 12113 (1.2%) zerosZeros

Reproduction

Analysis started2024-01-25 23:27:27.081888
Analysis finished2024-01-25 23:31:07.213946
Duration3 minutes and 40.13 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

EventID
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct1000000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499999.5
Minimum0
Maximum999999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2024-01-26T01:31:07.292086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49999.95
Q1249999.75
median499999.5
Q3749999.25
95-th percentile949999.05
Maximum999999
Range999999
Interquartile range (IQR)499999.5

Descriptive statistics

Standard deviation288675.28
Coefficient of variation (CV)0.57735114
Kurtosis-1.2
Mean499999.5
Median Absolute Deviation (MAD)250000
Skewness-2.5117903 × 10-15
Sum4.999995 × 1011
Variance8.3333417 × 1010
MonotonicityStrictly increasing
2024-01-26T01:31:07.402263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
666657 1
 
< 0.1%
666659 1
 
< 0.1%
666660 1
 
< 0.1%
666661 1
 
< 0.1%
666662 1
 
< 0.1%
666663 1
 
< 0.1%
666664 1
 
< 0.1%
666665 1
 
< 0.1%
666666 1
 
< 0.1%
Other values (999990) 999990
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
999999 1
< 0.1%
999998 1
< 0.1%
999997 1
< 0.1%
999996 1
< 0.1%
999995 1
< 0.1%
999994 1
< 0.1%
999993 1
< 0.1%
999992 1
< 0.1%
999991 1
< 0.1%
999990 1
< 0.1%

CustID
Real number (ℝ)

Distinct5000
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1683.5171
Minimum0
Maximum4999
Zeros7086
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2024-01-26T01:31:07.512443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q1333
median1277
Q32830
95-th percentile4516
Maximum4999
Range4999
Interquartile range (IQR)2497

Descriptive statistics

Standard deviation1487.6956
Coefficient of variation (CV)0.88368306
Kurtosis-0.86694302
Mean1683.5171
Median Absolute Deviation (MAD)1090
Skewness0.62799562
Sum1.6835171 × 109
Variance2213238.1
MonotonicityNot monotonic
2024-01-26T01:31:07.622228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7086
 
0.7%
1 5075
 
0.5%
2 4064
 
0.4%
3 3438
 
0.3%
4 3261
 
0.3%
5 2960
 
0.3%
6 2655
 
0.3%
7 2498
 
0.2%
8 2311
 
0.2%
9 2276
 
0.2%
Other values (4990) 964376
96.4%
ValueCountFrequency (%)
0 7086
0.7%
1 5075
0.5%
2 4064
0.4%
3 3438
0.3%
4 3261
0.3%
5 2960
0.3%
6 2655
 
0.3%
7 2498
 
0.2%
8 2311
 
0.2%
9 2276
 
0.2%
ValueCountFrequency (%)
4999 105
< 0.1%
4998 98
< 0.1%
4997 83
< 0.1%
4996 97
< 0.1%
4995 103
< 0.1%
4994 100
< 0.1%
4993 101
< 0.1%
4992 115
< 0.1%
4991 95
< 0.1%
4990 93
< 0.1%

TrackId
Real number (ℝ)

ZEROS 

Distinct1700
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean576.26831
Minimum0
Maximum1699
Zeros12113
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2024-01-26T01:31:07.732006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q1117
median440
Q3968
95-th percentile1536
Maximum1699
Range1699
Interquartile range (IQR)851

Descriptive statistics

Standard deviation505.82306
Coefficient of variation (CV)0.87775616
Kurtosis-0.87966295
Mean576.26831
Median Absolute Deviation (MAD)373
Skewness0.61914527
Sum5.7626831 × 108
Variance255856.97
MonotonicityNot monotonic
2024-01-26T01:31:07.841783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12113
 
1.2%
1 8689
 
0.9%
2 7028
 
0.7%
3 6172
 
0.6%
4 5527
 
0.6%
5 5100
 
0.5%
6 4607
 
0.5%
7 4347
 
0.4%
8 4092
 
0.4%
9 3926
 
0.4%
Other values (1690) 938399
93.8%
ValueCountFrequency (%)
0 12113
1.2%
1 8689
0.9%
2 7028
0.7%
3 6172
0.6%
4 5527
0.6%
5 5100
0.5%
6 4607
 
0.5%
7 4347
 
0.4%
8 4092
 
0.4%
9 3926
 
0.4%
ValueCountFrequency (%)
1699 308
< 0.1%
1698 296
< 0.1%
1697 321
< 0.1%
1696 337
< 0.1%
1695 324
< 0.1%
1694 296
< 0.1%
1693 282
< 0.1%
1692 286
< 0.1%
1691 290
< 0.1%
1690 331
< 0.1%
Distinct130988
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
Minimum2014-10-01 00:00:00
Maximum2014-12-30 23:59:00
2024-01-26T01:31:07.967183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:31:08.076958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Mobile
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
1
607765 
0
392235 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 607765
60.8%
0 392235
39.2%

Length

2024-01-26T01:31:08.200243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-26T01:31:08.375068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 607765
60.8%
0 392235
39.2%

Most occurring characters

ValueCountFrequency (%)
1 607765
60.8%
0 392235
39.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 607765
60.8%
0 392235
39.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1000000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 607765
60.8%
0 392235
39.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 607765
60.8%
0 392235
39.2%

ZipCode
Real number (ℝ)

Distinct41241
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49347.244
Minimum501
Maximum99403
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2024-01-26T01:31:08.469222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum501
5-th percentile6365
Q126167
median48674
Q372532
95-th percentile94624
Maximum99403
Range98902
Interquartile range (IQR)46365

Descriptive statistics

Standard deviation27537.664
Coefficient of variation (CV)0.55803855
Kurtosis-1.1144623
Mean49347.244
Median Absolute Deviation (MAD)23337
Skewness0.069503229
Sum4.9347244 × 1010
Variance7.5832296 × 108
MonotonicityNot monotonic
2024-01-26T01:31:08.594634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12084 48
 
< 0.1%
66636 45
 
< 0.1%
97049 45
 
< 0.1%
34235 45
 
< 0.1%
27264 44
 
< 0.1%
33931 44
 
< 0.1%
16839 44
 
< 0.1%
62473 44
 
< 0.1%
28217 44
 
< 0.1%
60652 44
 
< 0.1%
Other values (41231) 999553
> 99.9%
ValueCountFrequency (%)
501 24
< 0.1%
544 32
< 0.1%
1001 22
< 0.1%
1002 38
< 0.1%
1003 34
< 0.1%
1004 18
< 0.1%
1005 20
< 0.1%
1007 19
< 0.1%
1008 23
< 0.1%
1009 24
< 0.1%
ValueCountFrequency (%)
99403 24
< 0.1%
99402 25
< 0.1%
99401 24
< 0.1%
99371 27
< 0.1%
99363 24
< 0.1%
99362 21
< 0.1%
99361 27
< 0.1%
99360 29
< 0.1%
99359 19
< 0.1%
99357 29
< 0.1%

Title
Text

MISSING 

Distinct1325
Distinct (%)0.2%
Missing139876
Missing (%)14.0%
Memory size7.6 MiB
2024-01-26T01:31:08.830194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length33
Mean length14.617727
Min length3

Characters and Unicode

Total characters12573058
Distinct characters63
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStrange Magic
2nd rowMoney Talks
3rd rowBig Ten Inch Record
4th rowThe Ripper
5th rowWelcome To The Boomtown
ValueCountFrequency (%)
the 141785
 
5.6%
in 67553
 
2.7%
you 60417
 
2.4%
on 48820
 
1.9%
of 45397
 
1.8%
to 44070
 
1.7%
a 43772
 
1.7%
love 34454
 
1.4%
me 33122
 
1.3%
rock 29876
 
1.2%
Other values (1391) 1999110
78.4%
2024-01-26T01:31:09.191182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1699372
 
13.5%
e 1101890
 
8.8%
o 849324
 
6.8%
n 694516
 
5.5%
a 633815
 
5.0%
t 544215
 
4.3%
i 541449
 
4.3%
r 492237
 
3.9%
l 427958
 
3.4%
h 371978
 
3.0%
Other values (53) 5216304
41.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7883914
62.7%
Uppercase Letter 2934281
 
23.3%
Space Separator 1699372
 
13.5%
Decimal Number 55491
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1101890
14.0%
o 849324
10.8%
n 694516
 
8.8%
a 633815
 
8.0%
t 544215
 
6.9%
i 541449
 
6.9%
r 492237
 
6.2%
l 427958
 
5.4%
h 371978
 
4.7%
s 326211
 
4.1%
Other values (16) 1900321
24.1%
Uppercase Letter
ValueCountFrequency (%)
T 275110
 
9.4%
S 209884
 
7.2%
L 196539
 
6.7%
A 175513
 
6.0%
I 173236
 
5.9%
O 172656
 
5.9%
R 170922
 
5.8%
M 160249
 
5.5%
B 159182
 
5.4%
H 157076
 
5.4%
Other values (16) 1083914
36.9%
Decimal Number
ValueCountFrequency (%)
1 16716
30.1%
9 13363
24.1%
8 9105
16.4%
6 3871
 
7.0%
7 2897
 
5.2%
4 2809
 
5.1%
2 2228
 
4.0%
3 2104
 
3.8%
5 1511
 
2.7%
0 887
 
1.6%
Space Separator
ValueCountFrequency (%)
1699372
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10818195
86.0%
Common 1754863
 
14.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1101890
 
10.2%
o 849324
 
7.9%
n 694516
 
6.4%
a 633815
 
5.9%
t 544215
 
5.0%
i 541449
 
5.0%
r 492237
 
4.6%
l 427958
 
4.0%
h 371978
 
3.4%
s 326211
 
3.0%
Other values (42) 4834602
44.7%
Common
ValueCountFrequency (%)
1699372
96.8%
1 16716
 
1.0%
9 13363
 
0.8%
8 9105
 
0.5%
6 3871
 
0.2%
7 2897
 
0.2%
4 2809
 
0.2%
2 2228
 
0.1%
3 2104
 
0.1%
5 1511
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12573058
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1699372
 
13.5%
e 1101890
 
8.8%
o 849324
 
6.8%
n 694516
 
5.5%
a 633815
 
5.0%
t 544215
 
4.3%
i 541449
 
4.3%
r 492237
 
3.9%
l 427958
 
3.4%
h 371978
 
3.0%
Other values (53) 5216304
41.5%

Artist
Text

MISSING 

Distinct405
Distinct (%)< 0.1%
Missing139876
Missing (%)14.0%
Memory size7.6 MiB
2024-01-26T01:31:09.426721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9136
Median length44
Mean length16.517932
Min length3

Characters and Unicode

Total characters14207470
Distinct characters76
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowElectric Light Orchestra
2nd rowAC/DC
3rd rowAerosmith
4th rowJudas Priest
5th rowDavid & David
ValueCountFrequency (%)
ac/dc 66532
 
3.2%
the 60727
 
3.0%
aerosmith 51197
 
2.5%
38 34002
 
1.7%
special 34002
 
1.7%
billy 29264
 
1.4%
band 27907
 
1.4%
bob 24915
 
1.2%
beatles 22382
 
1.1%
john 21856
 
1.1%
Other values (1598) 1684919
81.9%
2024-01-26T01:31:09.787662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1227129
 
8.6%
1060483
 
7.5%
o 742453
 
5.2%
a 695485
 
4.9%
n 636405
 
4.5%
r 635431
 
4.5%
l 621649
 
4.4%
i 618513
 
4.4%
t 508210
 
3.6%
s 435640
 
3.1%
Other values (66) 7026072
49.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8398206
59.1%
Uppercase Letter 2989774
 
21.0%
Space Separator 1060483
 
7.5%
Decimal Number 1036070
 
7.3%
Other Punctuation 571432
 
4.0%
Control 141203
 
1.0%
Dash Punctuation 9586
 
0.1%
Open Punctuation 358
 
< 0.1%
Close Punctuation 358
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1227129
14.6%
o 742453
 
8.8%
a 695485
 
8.3%
n 636405
 
7.6%
r 635431
 
7.6%
l 621649
 
7.4%
i 618513
 
7.4%
t 508210
 
6.1%
s 435640
 
5.2%
h 327678
 
3.9%
Other values (17) 1949613
23.2%
Uppercase Letter
ValueCountFrequency (%)
C 310726
 
10.4%
B 283874
 
9.5%
S 268342
 
9.0%
A 251567
 
8.4%
D 218936
 
7.3%
E 189434
 
6.3%
T 170610
 
5.7%
R 140799
 
4.7%
L 133184
 
4.5%
M 110416
 
3.7%
Other values (17) 911886
30.5%
Decimal Number
ValueCountFrequency (%)
1 200538
19.4%
3 183318
17.7%
2 154890
14.9%
8 112363
10.8%
4 79964
 
7.7%
6 67980
 
6.6%
0 66326
 
6.4%
9 60744
 
5.9%
5 56019
 
5.4%
7 53928
 
5.2%
Other Punctuation
ValueCountFrequency (%)
, 423609
74.1%
/ 67778
 
11.9%
. 48902
 
8.6%
& 16505
 
2.9%
' 6782
 
1.2%
? 5004
 
0.9%
" 2852
 
0.5%
Space Separator
ValueCountFrequency (%)
1060483
100.0%
Control
ValueCountFrequency (%)
141203
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9586
100.0%
Open Punctuation
ValueCountFrequency (%)
( 358
100.0%
Close Punctuation
ValueCountFrequency (%)
) 358
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11387980
80.2%
Common 2819490
 
19.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1227129
 
10.8%
o 742453
 
6.5%
a 695485
 
6.1%
n 636405
 
5.6%
r 635431
 
5.6%
l 621649
 
5.5%
i 618513
 
5.4%
t 508210
 
4.5%
s 435640
 
3.8%
h 327678
 
2.9%
Other values (44) 4939387
43.4%
Common
ValueCountFrequency (%)
1060483
37.6%
, 423609
 
15.0%
1 200538
 
7.1%
3 183318
 
6.5%
2 154890
 
5.5%
141203
 
5.0%
8 112363
 
4.0%
4 79964
 
2.8%
6 67980
 
2.4%
/ 67778
 
2.4%
Other values (12) 327364
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14205210
> 99.9%
None 2260
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1227129
 
8.6%
1060483
 
7.5%
o 742453
 
5.2%
a 695485
 
4.9%
n 636405
 
4.5%
r 635431
 
4.5%
l 621649
 
4.4%
i 618513
 
4.4%
t 508210
 
3.6%
s 435640
 
3.1%
Other values (64) 7023812
49.4%
None
ValueCountFrequency (%)
Ö 1867
82.6%
ÿ 393
 
17.4%

Length
Real number (ℝ)

MISSING 

Distinct240
Distinct (%)< 0.1%
Missing139876
Missing (%)14.0%
Infinite0
Infinite (%)0.0%
Mean237.13538
Minimum120
Maximum359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2024-01-26T01:31:09.896934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile131
Q1180
median232
Q3299
95-th percentile345
Maximum359
Range239
Interquartile range (IQR)119

Descriptive statistics

Standard deviation68.858821
Coefficient of variation (CV)0.29037768
Kurtosis-1.2023644
Mean237.13538
Median Absolute Deviation (MAD)60
Skewness0.060875619
Sum2.0396583 × 108
Variance4741.5373
MonotonicityNot monotonic
2024-01-26T01:31:10.007106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 13102
 
1.3%
269 11763
 
1.2%
219 10241
 
1.0%
143 9645
 
1.0%
221 9472
 
0.9%
341 9403
 
0.9%
334 8738
 
0.9%
154 8535
 
0.9%
163 8463
 
0.8%
135 7966
 
0.8%
Other values (230) 762796
76.3%
(Missing) 139876
 
14.0%
ValueCountFrequency (%)
120 2437
 
0.2%
121 1053
 
0.1%
122 3979
0.4%
123 5150
0.5%
124 3190
0.3%
125 1318
 
0.1%
126 1751
 
0.2%
127 6974
0.7%
128 5942
0.6%
129 3092
0.3%
ValueCountFrequency (%)
359 1831
 
0.2%
358 2643
0.3%
357 2877
0.3%
356 4246
0.4%
355 5130
0.5%
354 1609
 
0.2%
353 2289
0.2%
352 3788
0.4%
351 2619
0.3%
350 3369
0.3%

Name
Text

Distinct4946
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
2024-01-26T01:31:10.242271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length19
Mean length13.064705
Min length7

Characters and Unicode

Total characters13064705
Distinct characters52
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLucas Pizano
2nd rowKenneth Rodgers
3rd rowCarlos Kirk
4th rowCharlene Boyd
5th rowMary Decker
ValueCountFrequency (%)
james 23853
 
1.2%
robert 19330
 
1.0%
david 17934
 
0.9%
john 16101
 
0.8%
mary 14755
 
0.7%
william 14321
 
0.7%
smith 12018
 
0.6%
brown 11792
 
0.6%
richard 11775
 
0.6%
michael 11721
 
0.6%
Other values (3920) 1846400
92.3%
2024-01-26T01:31:10.571603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1265195
 
9.7%
a 1193996
 
9.1%
r 1003801
 
7.7%
1000000
 
7.7%
n 934548
 
7.2%
i 775493
 
5.9%
o 756781
 
5.8%
l 707165
 
5.4%
s 498850
 
3.8%
t 483811
 
3.7%
Other values (42) 4445065
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10064705
77.0%
Uppercase Letter 2000000
 
15.3%
Space Separator 1000000
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1265195
12.6%
a 1193996
11.9%
r 1003801
10.0%
n 934548
9.3%
i 775493
 
7.7%
o 756781
 
7.5%
l 707165
 
7.0%
s 498850
 
5.0%
t 483811
 
4.8%
h 361616
 
3.6%
Other values (16) 2083449
20.7%
Uppercase Letter
ValueCountFrequency (%)
M 189453
 
9.5%
J 173452
 
8.7%
S 152426
 
7.6%
C 144375
 
7.2%
B 132754
 
6.6%
R 132114
 
6.6%
D 116408
 
5.8%
G 98285
 
4.9%
A 97050
 
4.9%
W 96708
 
4.8%
Other values (15) 666975
33.3%
Space Separator
ValueCountFrequency (%)
1000000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12064705
92.3%
Common 1000000
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1265195
 
10.5%
a 1193996
 
9.9%
r 1003801
 
8.3%
n 934548
 
7.7%
i 775493
 
6.4%
o 756781
 
6.3%
l 707165
 
5.9%
s 498850
 
4.1%
t 483811
 
4.0%
h 361616
 
3.0%
Other values (41) 4083449
33.8%
Common
ValueCountFrequency (%)
1000000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13064705
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1265195
 
9.7%
a 1193996
 
9.1%
r 1003801
 
7.7%
1000000
 
7.7%
n 934548
 
7.2%
i 775493
 
5.9%
o 756781
 
5.8%
l 707165
 
5.4%
s 498850
 
3.8%
t 483811
 
3.7%
Other values (42) 4445065
34.0%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
0
605868 
1
394132 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 605868
60.6%
1 394132
39.4%

Length

2024-01-26T01:31:10.665762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-26T01:31:10.744291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 605868
60.6%
1 394132
39.4%

Most occurring characters

ValueCountFrequency (%)
0 605868
60.6%
1 394132
39.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 605868
60.6%
1 394132
39.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1000000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 605868
60.6%
1 394132
39.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 605868
60.6%
1 394132
39.4%
Distinct5000
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
2024-01-26T01:31:10.979967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length32
Mean length23.045462
Min length16

Characters and Unicode

Total characters23045462
Distinct characters60
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2723 Stony Beaver Hollow
2nd row74413 Heather Elm Bluff
3rd row14 Hidden Bear Circle
4th row5967 Stony Branch Stroll
5th row16355 Pretty Panda Pike
ValueCountFrequency (%)
bluff 24115
 
0.6%
gate 23299
 
0.6%
villa 22007
 
0.6%
round 20539
 
0.5%
dale 20333
 
0.5%
easy 20127
 
0.5%
view 19409
 
0.5%
hickory 18762
 
0.5%
grove 18738
 
0.5%
vale 18412
 
0.5%
Other values (4965) 3794259
94.9%
2024-01-26T01:31:11.341421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3000000
 
13.0%
e 1754870
 
7.6%
a 1187818
 
5.2%
r 1099797
 
4.8%
n 1029253
 
4.5%
o 1007033
 
4.4%
l 937214
 
4.1%
t 760803
 
3.3%
i 725636
 
3.1%
1 554502
 
2.4%
Other values (50) 10988536
47.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12491665
54.2%
Decimal Number 4553797
 
19.8%
Space Separator 3000000
 
13.0%
Uppercase Letter 3000000
 
13.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1754870
14.0%
a 1187818
9.5%
r 1099797
 
8.8%
n 1029253
 
8.2%
o 1007033
 
8.1%
l 937214
 
7.5%
t 760803
 
6.1%
i 725636
 
5.8%
d 551244
 
4.4%
u 515578
 
4.1%
Other values (15) 2922419
23.4%
Uppercase Letter
ValueCountFrequency (%)
B 307561
 
10.3%
C 289044
 
9.6%
S 286087
 
9.5%
P 233871
 
7.8%
R 218032
 
7.3%
H 194661
 
6.5%
L 178448
 
5.9%
T 171053
 
5.7%
G 151571
 
5.1%
A 132629
 
4.4%
Other values (14) 837043
27.9%
Decimal Number
ValueCountFrequency (%)
1 554502
12.2%
2 489314
10.7%
4 488163
10.7%
5 468402
10.3%
3 462599
10.2%
7 434752
9.5%
6 428963
9.4%
8 426936
9.4%
9 416565
9.1%
0 383601
8.4%
Space Separator
ValueCountFrequency (%)
3000000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15491665
67.2%
Common 7553797
32.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1754870
 
11.3%
a 1187818
 
7.7%
r 1099797
 
7.1%
n 1029253
 
6.6%
o 1007033
 
6.5%
l 937214
 
6.0%
t 760803
 
4.9%
i 725636
 
4.7%
d 551244
 
3.6%
u 515578
 
3.3%
Other values (39) 5922419
38.2%
Common
ValueCountFrequency (%)
3000000
39.7%
1 554502
 
7.3%
2 489314
 
6.5%
4 488163
 
6.5%
5 468402
 
6.2%
3 462599
 
6.1%
7 434752
 
5.8%
6 428963
 
5.7%
8 426936
 
5.7%
9 416565
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23045462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3000000
 
13.0%
e 1754870
 
7.6%
a 1187818
 
5.2%
r 1099797
 
4.8%
n 1029253
 
4.5%
o 1007033
 
4.4%
l 937214
 
4.1%
t 760803
 
3.3%
i 725636
 
3.1%
1 554502
 
2.4%
Other values (50) 10988536
47.7%

zip
Real number (ℝ)

Distinct4695
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50410.68
Minimum1002
Maximum99347
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2024-01-26T01:31:11.451536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1002
5-th percentile6858
Q127358
median50207
Q373156
95-th percentile95229
Maximum99347
Range98345
Interquartile range (IQR)45798

Descriptive statistics

Standard deviation27648.099
Coefficient of variation (CV)0.54845718
Kurtosis-1.1382585
Mean50410.68
Median Absolute Deviation (MAD)22925
Skewness0.030620293
Sum5.041068 × 1010
Variance7.6441739 × 108
MonotonicityNot monotonic
2024-01-26T01:31:11.561334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72132 7213
 
0.7%
17307 5075
 
0.5%
66216 4064
 
0.4%
36690 3438
 
0.3%
61377 3261
 
0.3%
20115 2960
 
0.3%
15330 2655
 
0.3%
33570 2498
 
0.2%
41240 2311
 
0.2%
63565 2276
 
0.2%
Other values (4685) 964249
96.4%
ValueCountFrequency (%)
1002 437
< 0.1%
1037 92
 
< 0.1%
1066 157
 
< 0.1%
1082 162
 
< 0.1%
1092 168
 
< 0.1%
1115 110
 
< 0.1%
1195 107
 
< 0.1%
1235 195
 
< 0.1%
1253 832
0.1%
1256 226
 
< 0.1%
ValueCountFrequency (%)
99347 129
 
< 0.1%
99346 178
 
< 0.1%
99330 99
 
< 0.1%
99256 1027
0.1%
99223 111
 
< 0.1%
99206 98
 
< 0.1%
99176 141
 
< 0.1%
99173 117
 
< 0.1%
99158 289
 
< 0.1%
99153 434
< 0.1%
Distinct455
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
Minimum2011-05-10 00:00:00
Maximum2013-07-31 00:00:00
2024-01-26T01:31:11.671020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:31:11.780794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Status
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
1
900436 
0
99564 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 900436
90.0%
0 99564
 
10.0%

Length

2024-01-26T01:31:11.874945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-26T01:31:11.953471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 900436
90.0%
0 99564
 
10.0%

Most occurring characters

ValueCountFrequency (%)
1 900436
90.0%
0 99564
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 900436
90.0%
0 99564
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 900436
90.0%
0 99564
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 900436
90.0%
0 99564
 
10.0%

Level
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
0
565316 
1
332656 
2
102028 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 565316
56.5%
1 332656
33.3%
2 102028
 
10.2%

Length

2024-01-26T01:31:12.031997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-26T01:31:12.110812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 565316
56.5%
1 332656
33.3%
2 102028
 
10.2%

Most occurring characters

ValueCountFrequency (%)
0 565316
56.5%
1 332656
33.3%
2 102028
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 565316
56.5%
1 332656
33.3%
2 102028
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1000000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 565316
56.5%
1 332656
33.3%
2 102028
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 565316
56.5%
1 332656
33.3%
2 102028
 
10.2%

Campaign
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
1
596844 
4
142050 
0
118043 
3
95621 
2
 
47442

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 596844
59.7%
4 142050
 
14.2%
0 118043
 
11.8%
3 95621
 
9.6%
2 47442
 
4.7%

Length

2024-01-26T01:31:12.204657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-26T01:31:12.283476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 596844
59.7%
4 142050
 
14.2%
0 118043
 
11.8%
3 95621
 
9.6%
2 47442
 
4.7%

Most occurring characters

ValueCountFrequency (%)
1 596844
59.7%
4 142050
 
14.2%
0 118043
 
11.8%
3 95621
 
9.6%
2 47442
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 596844
59.7%
4 142050
 
14.2%
0 118043
 
11.8%
3 95621
 
9.6%
2 47442
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1000000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 596844
59.7%
4 142050
 
14.2%
0 118043
 
11.8%
3 95621
 
9.6%
2 47442
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 596844
59.7%
4 142050
 
14.2%
0 118043
 
11.8%
3 95621
 
9.6%
2 47442
 
4.7%

LinkedWithApps
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
1
504687 
0
495313 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 504687
50.5%
0 495313
49.5%

Length

2024-01-26T01:31:12.377887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-26T01:31:12.441017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 504687
50.5%
0 495313
49.5%

Most occurring characters

ValueCountFrequency (%)
1 504687
50.5%
0 495313
49.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 504687
50.5%
0 495313
49.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1000000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 504687
50.5%
0 495313
49.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 504687
50.5%
0 495313
49.5%

Interactions

2024-01-26T01:31:03.068553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:30:57.729381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:30:58.778009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:30:59.832895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:31:00.995183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:31:02.048070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:31:03.256981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:30:57.917604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:30:58.953861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:31:00.021258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:31:01.183703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:31:02.220995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:31:03.430237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:30:58.076278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:30:59.110958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:31:00.194141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:31:01.355909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:31:02.386685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:31:03.618608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:30:58.262605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:30:59.315032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:31:00.461405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:31:01.529556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:31:02.550800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:31:03.775584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:30:58.420125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:30:59.479670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:31:00.634134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:31:01.702356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:31:02.723534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:31:03.948489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:30:58.593090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:30:59.660151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:31:00.822496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:31:01.885025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-26T01:31:02.887724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-26T01:31:12.509209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CampaignCustIDEventIDGenderLengthLevelLinkedWithAppsMobileStatusTrackIdZipCodezip
Campaign1.0000.0260.0010.029-0.0010.0540.0430.0150.0310.0010.001-0.018
CustID0.0261.0000.0000.046-0.0000.0390.0380.0100.037-0.0000.001-0.018
EventID0.0010.0001.0000.0000.0030.0000.0010.0020.0020.001-0.001-0.001
Gender0.0290.0460.0001.000-0.0020.0080.0070.0010.008-0.001-0.000-0.021
Length-0.001-0.0000.003-0.0021.0000.0000.0020.0000.0010.0520.000-0.000
Level0.0540.0390.0000.0080.0001.0000.0530.2590.046-0.0010.0020.004
LinkedWithApps0.0430.0380.0010.0070.0020.0531.0000.0140.0190.0010.001-0.003
Mobile0.0150.0100.0020.0010.0000.2590.0141.0000.011-0.0010.0000.000
Status0.0310.0370.0020.0080.0010.0460.0190.0111.000-0.000-0.000-0.006
TrackId0.001-0.0000.001-0.0010.052-0.0010.001-0.001-0.0001.000-0.001-0.001
ZipCode0.0010.001-0.001-0.0000.0000.0020.0010.000-0.000-0.0011.0000.000
zip-0.018-0.018-0.001-0.021-0.0000.004-0.0030.000-0.006-0.0010.0001.000

Missing values

2024-01-26T01:31:04.183670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-26T01:31:04.967994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-01-26T01:31:06.412871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

EventIDCustIDTrackIdDateTimeMobileZipCodeTitleArtistLengthNameGenderAddresszipSignDateStatusLevelCampaignLinkedWithApps
004845310/23/14 3:26072132Strange MagicElectric Light Orchestra170.0Lucas Pizano02723 Stony Beaver Hollow9925611/02/20121031
1110811910/15/14 18:32117307Money TalksAC/DC323.0Kenneth Rodgers074413 Heather Elm Bluff3030105/14/20130111
225323612/10/14 15:33166216Big Ten Inch RecordAerosmith204.0Carlos Kirk014 Hidden Bear Circle9074507/14/20130211
33264182210/20/14 2:24136690The RipperJudas Priest122.0Charlene Boyd05967 Stony Branch Stroll464503/26/20131110
44225133811/18/14 7:16161377Welcome To The BoomtownDavid & David269.0Mary Decker016355 Pretty Panda Pike4058006/23/20131101
551811611/18/14 2:00120115Loser3 Doors Down127.0Joshua Reyna0987 Cotton Maple Turn2457401/14/20131111
6636442412/12/14 15:24115330Shot Down In FlamesAC/DC326.0Cynthia Moses186610 Misty Nectar Drive2385706/25/20131010
7725072610/7/14 9:48033570Right Here Right NowJesus Jones232.0Nell Rosenblum118820 Birch Mill Twist7244312/05/20121011
88178244212/30/14 15:27141240Baby Hold OnEddie Money298.0Jason Witzel076 Jagged Barn Turn2808607/19/20131231
99293277511/12/14 7:45063565On the Dark SideJohn Cafferty213.0Darlene Rahn04 Tawny Beaver Ledge773705/11/20131010
EventIDCustIDTrackIdDateTimeMobileZipCodeTitleArtistLengthNameGenderAddresszipSignDateStatusLevelCampaignLinkedWithApps
999990999990209123510/21/14 2:38014723NaNNaNNaNBilly Gifford04 White Hickory Falls5441312/08/20120041
99999199999123651011/2/14 0:0311266All The Kings HorsesFirm301.0Johnny Moore136198 Cozy Turtle Glen6521206/22/20131010
999992999992101783512/8/14 7:0616020NaNNaNNaNJoshua Waldron026401 Heather Castle Circle2019506/27/20131010
999993999993388430310/22/14 12:34145264White RoomCream344.0Terry Mulligan089241 White Prairie Wander8731907/31/20130011
9999949999941364910/20/14 19:37127822No More No MoreAerosmith145.0Dona Casey151613 Burning Anchor Haven3152010/02/20121211
999995999995336314410/23/14 3:58150302The StrangerBilly Joel122.0Bobbie Wingfield149345 Rough Sky Stead2072407/21/20131210
99999699999621050711/18/14 0:20175550Stay With MeFaces324.0Francis Ryan161901 Amber Island Falls1552007/11/20131010
999997999997751139711/3/14 9:36129912NaNNaNNaNCarrie King122811 Sleepy Elk Mews7457701/11/20131010
9999989999984717132810/26/14 4:04158425NaNNaNNaNRachel Dvorak078831 Round Shadow Lane3106803/19/20131031
999999999999301437412/10/14 19:46129856Armageddon ItDef Leppard173.0Craig Mcpherson035949 Dusty Dove Heights5050105/04/20131241